Lactobacillus spp. attenuate antibiotic-induced immune and microbiota dysregulation in honey bees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Widespread antibiotic usage in apiculture contributes substantially to the global dissemination of antimicrobial resistance and has the potential to negatively influence bacterial symbionts of honey bees (Apis mellifera). Here, we show that routine antibiotic administration with oxytetracycline selectively increased tetB (efflux pump resistance gene) abundance in the gut microbiota of adult workers while concurrently depleting several key symbionts known to regulate immune function and nutrient metabolism such as Frischella perrera and Lactobacillus Firm-5 strains. These microbial changes were functionally characterized by decreased capped brood counts (marker of hive nutritional status and productivity) and reduced antimicrobial capacity of adult hemolymph (indicator of immune competence). Importantly, combination therapy with three immunostimulatory Lactobacillus strains could mitigate antibiotic-associated microbiota dysbiosis and immune deficits in adult workers, as well as maximize the intended benefit of oxytetracycline by suppressing larval pathogen loads to near-undetectable levels. We conclude that microbial-based therapeutics may offer a simple but effective solution to reduce honey bee disease burden, environmental xenobiotic exposure, and spread of antimicrobial resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it